Résultats de la recherche - (coding OR decoding) (algorithm OR Algorithmssssic)

Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning par Li, Xinran, Pan, Ling, Zhang, Jun

Table des matières: “… in the context of actor-critic algorithms, which could help improve value estimations.Our empirical evaluations…”
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Optimal Inferential Control of Convolutional Neural Networks par Vaziri, Ali, Fang, Huazhen

Table des matières: “… leverage ensemble Kalman smoothing, a sequential Monte Carlo algorithm, to take advantage of its…”
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LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition par Liu, Huan, Yang, Shusen, Zhang, Yuzhe, Wang, Mengze, Gong, Fanyu, Xie, Chengxi, Liu, Guanjian, Liu, Zejun, Liu, Yong-Jin, Lu, Bao-Liang, Zhang, Dalin

Table des matières: “… benchmark and algorithm library designed to facilitate fair comparisons in EER. LibEER carefully selects…”
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AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization par Wu, Junkang, Wang, Xue, Yang, Zhengyi, Wu, Jiancan, Gao, Jinyang, Ding, Bolin, Wang, Xiang, He, Xiangnan

Table des matières: “… preference optimization algorithm designed to address these limitations by introducing a dynamic reward…”
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AutoTurb: Using Large Language Models for Automatic Algebraic Model Discovery of Turbulence Closure par Zhang, Yu, Zheng, Kefeng, Liu, Fei, Zhang, Qingfu, Wang, Zhenkun

Table des matières: “…. The deduced EARSM can be readily implemented in existing computational fluid dynamic (CFD) codes and promotes…”
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POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference Optimization par Karaman, Batuhan K., Zabir, Ishmam, Benhaim, Alon, Chaudhary, Vishrav, Sabuncu, Mert R., Song, Xia

Table des matières: “… that are highly safe but prone to overrefusal. POROver employs preference optimization algorithms and leverages…”
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Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling par Holthusen, Hagen, Brepols, Tim, Linka, Kevin, Kuhl, Ellen

Table des matières: “…. To address this challenge, built-in physics machine learning algorithms offer significant potential…”
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MotionBank: A Large-scale Video Motion Benchmark with Disentangled Rule-based Annotations par Xu, Liang, Hua, Shaoyang, Lin, Zili, Liu, Yifan, Ma, Feipeng, Yan, Yichao, Jin, Xin, Yang, Xiaokang, Zeng, Wenjun

Table des matières: “… better motion text alignment, we also meticulously devise a motion caption generation algorithm…”
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Ensemble-based, large-eddy reconstruction of wind turbine inflow in a near-stationary atmospheric boundary layer through generative artificial intelligence par Rybchuk, Alex, Martínez-Tossas, Luis A., Letizia, Stefano, Hamilton, Nicholas, Scholbrock, Andy, Maric, Emina, Houck, Daniel R., Herges, Thomas G., de Velder, Nathaniel B., Doubrawa, Paula

Table des matières: “… and atmospheric model information through a diffusion model machine learning algorithm, allowing us to generate…”
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Agent Skill Acquisition for Large Language Models via CycleQD par Kuroki, So, Nakamura, Taishi, Akiba, Takuya, Tang, Yujin

Table des matières: “… of the algorithm, along with a model merging based crossover and an SVD-based mutation. In CycleQD, each task…”
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AlphaChimp: Tracking and Behavior Recognition of Chimpanzees par Ma, Xiaoxuan, Lin, Yutang, Xu, Yuan, Kaufhold, Stephan P., Terwilliger, Jack, Meza, Andres, Zhu, Yixin, Rossano, Federico, Wang, Yizhou

Table des matières: “… of their social interactions and the lack of specialized algorithms. Existing methods often struggle…”
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Brain-like Variational Inference par Vafaii, Hadi, Galor, Dekel, Yates, Jacob L.

Table des matières: “… and Gaussian-based predictive coding models in sparsity, reconstruction, and biological plausibility…”
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Energy-Based Diffusion Language Models for Text Generation par Xu, Minkai, Geffner, Tomas, Kreis, Karsten, Nie, Weili, Xu, Yilun, Leskovec, Jure, Ermon, Stefano, Vahdat, Arash

Table des matières: “… propose an efficient generation algorithm via parallel important sampling. Comprehensive experiments…”
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Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning par Luo, Jianlan, Xu, Charles, Wu, Jeffrey, Levine, Sergey

Table des matières: “… coordination. Our approach integrates demonstrations and human corrections, efficient RL algorithms, and other…”
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How Do Developers Use Type Inference: An Exploratory Study in Kotlin par Flint, Samuel W., Keshk, Ali M., Dyer, Robert, Bagheri, Hamid

Table des matières: “…Statically typed languages offer numerous benefits to developers, such as improved code quality…”
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Bridge-IF: Learning Inverse Protein Folding with Markov Bridges par Zhu, Yiheng, Wu, Jialu, Li, Qiuyi, Yan, Jiahuan, Yin, Mingze, Wu, Wei, Li, Mingyang, Ye, Jieping, Wang, Zheng, Wu, Jian

Table des matières: “… learning algorithms for this task has seen significant success, the prevailing approaches, which…”
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SSFold: Learning to Fold Arbitrary Crumpled Cloth Using Graph Dynamics from Human Demonstration par Zhou, Changshi, Xu, Haichuan, Hu, Jiarui, Luan, Feng, Wang, Zhipeng, Dong, Yanchao, Zhou, Yanmin, He, Bin

Table des matières: “… algorithm to gather and integrate human demonstration data into our novel end-to-end neural network…”
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FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data par Zhang, Yukun, Chen, Guanzhong, Xu, Zenglin, Wang, Jianyong, Zeng, Dun, Li, Junfan, Wang, Jinghua, Qi, Yuan, King, Irwin

Table des matières: “… in realistic datasets that challenges current FL algorithms. To address these gaps, this paper presents…”
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DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection par Li, Shawn, Gong, Huixian, Dong, Hao, Yang, Tiankai, Tu, Zhengzhong, Zhao, Yue

Table des matières: “…, and nine base OOD algorithms demonstrate that DPU significantly improves OOD detection performance, setting…”
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GROOT: Effective Design of Biological Sequences with Limited Experimental Data par Tran, Thanh V. T., Ngo, Nhat Khang, Nguyen, Viet Anh, Hy, Truong Son

Table des matières: “… algorithms toward optimal outputs. However, existing methods struggle when labeled data is limited…”
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